


_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							set()
     # keep track of all the paths to be checked
     snake_case											=							[[start]]

     # return path if start is goal
     if start == goal:
          return [start]

     # keeps looping until all possible paths have been checked
     while queue:
          # pop the first path from the queue
          snake_case											=							queue.pop(0							)
          # get the last node from the path
          snake_case											=							path[-1]
          if node not in explored:
               snake_case											=							graph[node]
               # go through all neighbour nodes, construct a new path and
               # push it into the queue
               for neighbour in neighbours:
                    snake_case											=							list(UpperCamelCase_							)
                    new_path.append(UpperCamelCase_							)
                    queue.append(UpperCamelCase_							)
                    # return path if neighbour is goal
                    if neighbour == goal:
                         return new_path

            # mark node as explored
               explored.add(UpperCamelCase_							)

    # in case there's no path between the 2 nodes
     return []
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     if not graph or start not in graph or target not in graph:
          return -1
     if start == target:
          return 0
     snake_case											=							[start]
     snake_case											=							set(UpperCamelCase_							)
     # Keep tab on distances from `start` node.
     snake_case											=							{start: 0, target: -1}
     while queue:
          snake_case											=							queue.pop(0							)
          if node == target:
               snake_case											=							(
                   dist[node] if dist[target] == -1 else min(dist[target]				,dist[node]							)
               )
          for adjacent in graph[node]:
               if adjacent not in visited:
                    visited.add(UpperCamelCase_							)
                    queue.append(UpperCamelCase_							)
                    snake_case											=							dist[node] + 1
     return dist[target]


if __name__ == "__main__":
 print(bfs_shortest_path(demo_graph, "G", "D"))  # returns ['G', 'C', 'A', 'B', 'D']
 print(bfs_shortest_path_distance(demo_graph, "G", "D"))  # returns 4








import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
 from transformers import TvltImageProcessor

if is_speech_available():
 from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							'''ZinengTang/tvlt-base'''
         snake_case											=							tempfile.mkdtemp()
    def 						a_     (			self     ,   **__snake_case       ):
         return TvltImageProcessor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )
         processor.save_pretrained(self.tmpdirname       )
         snake_case											=							TvltProcessor.from_pretrained(self.tmpdirname       )

         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
         self.assertIsInstance(processor.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([1_2_0_0_0]       )

         snake_case											=							feature_extractor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(audio=__snake_case     ,   return_tensors='''np'''       )

         for key in audio_dict.keys():
              self.assertAlmostEqual(audio_dict[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([3, 2_2_4, 2_2_4]       )

         snake_case											=							image_processor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(images=__snake_case     ,   return_tensors='''np'''       )

         for key in image_dict.keys():
              self.assertAlmostEqual(image_dict[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([1_2_0_0_0]       )
         snake_case											=							np.ones([3, 2_2_4, 2_2_4]       )

         snake_case											=							processor(audio=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']       )

         # test if it raises when no input is passed
         with pytest.raises(__snake_case       ):
              processor()




    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         self.assertListEqual(
             processor.model_input_names     ,   image_processor.model_input_names + feature_extractor.model_input_names     ,   msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''     ,   )




